Orchestrating orphan ideas in the fuzzy front end of a large firm's R&D department
Bibliographic record
Abstract
The fuzzy front end is the critical initial step of an innovation process during which new ideas emerge. This step's functioning is well understood when the ideas produced are in line with the organisation's directions or roadmap. In that case, there are a variety of managerial methods available to guide, filter and control the development of ideas while reducing the associated risks. However, we know considerably less about the emergence and development of orphan ideas, that is ideas that are not aligned with the firm's strategic roadmap. Such orphan ideas are beyond the scope of managers because they are not consistent with the orientations and needs identified by the firm. The aim of this article is to start to fill this gap through the qualitative study of three unexpected ideation processes at Hydro‐Québec's research institute. Our data reveal a process characterised by a complicated intertwinement of formal and informal mechanisms and relationships. In particular, our results show that informal groups, which can be assimilated to epistemic communities, play a major role in the orchestration of the first stages of the journey of orphan ideas by taking charge of the development of the value proposition and of the idea's integration in the firm's managerial and strategic framework. Further, managing orphan ideas requires specific managerial devices and social mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".